Document management systems allow organizations to have greater control over the lifecycle of documents from creation through review, storage, retrieval and dissemination all the way to their destruction. Document management provides greater efficiencies in the ability to classify and reuse information. This document deals with issues of storage and retrieval processes of text documents in document management systems. Main focus is made on choosing an effective document format, means and methods for storing and retrieving documents. The paper suggests using XML as the base document format and relational database management system as backend storage of the document management system. A new modification of the standard Edge method for storing and retrieving XML documents from relational database management systems is introduced and the results of its performance experiment are presented. The experiment proves the performance superiority of the modified Edge method over its standard analogue.

There are many different database management systems (DBMSs) on the market today. They all have different strengths and weaknesses. What if all of these different DBMSs could be used together in a heterogeneous network? The purpose of this thesis is to explore ways of connecting the many different DBMSs together. This thesis will explore suitable architectures, features, and performance of such a network. This is all done in the context of Ericsson’s wireless communication network. This has not been done in this context before, and a big part of the thesis is exploring if it is even possible. The result of this thesis shows that it is not possible to find a solution that can fulfill the requirements of such a network in this context

Stored procedures are used as the current database logic for SAAB’s data model of the fighter aircraft JAS-39 Gripen electrical schemas. Since the database model was developed in 2000, a research and tests needed to be carried out to decide on whether updating the database to today's technology is applicable. Therefore, Object-Relational Mapping (ORM) is to be researched, tested and compared to stored procedures using test-driven development (TDD)concerning an important factor, that Stored procedures are well-known for, which is querying performance of the database. Moreover, how maintainability and flexibility [1] can affect decision between Stored procedures or migrating to ORM based on our subjective experience. NHibernate and Entity Framework are the two ORM solutions considered sinceSAAB uses C# in this project. The process of this project is run using scrum of the agile software development to maintain an iterative progress throughout the project timeline. In this paper, the process and methodology are covered in details and also the comparison with the test results. These results eventually lead us to the answer that ORM is not a suitable technology, and stored procedures still dominate the querying performance for SAAB’scurrent database.

No description available.

O operador de agrupamento e as funções de agregação são as principais ferramentas utilizadas para sumarizar dados em um Sistema de Gerenciamento de Base de Dados Relacionais (SGBDR). O operador de agrupamento funciona criando partições nos dados utilizando comparações por identidade, e permite que sejam aplicadas funções de agregação que retornam um único valor representando o grupo como um todo. Entretanto, para dados métricos, agrupamento utilizando identidade tem pouca utilidade. Neste caso, adotar o conceito de similaridade é frequentemente uma abordagem mais promissora. A literatura apresenta alguns operadores que podem agrupar os dados utilizando similaridade. Todos eles utilizam um limiar de valor de distância para atribuir os elementos aos grupos. No entanto, estes operadores não obtêm resultados satisfatórios quando a distribuição dos dados apresenta variações significativas na densidade de objetos em diferentes regiões do espaço. Para alcançar melhores resultados nestas situações, propusemos um novo operador que atribui os grupos utilizando uma eleição envolvendo grupos já atribuídos. Também propusemos generalizações, para os operadores existentes e propostos, para trabalhar com uma quantidade de vizinhos mais próximos e aproximação dos vizinhos mais próximos ao invés de um limiar de distância. Para possibilitar a inclusão destes operadores em SGBDR, propusemos uma extensão à Structured Query Language (SQL) e novas funções de agregação. Implementamos estes operadores em nosso framework em C++ usando a biblioteca Arboretum. Para avaliar os métodos propostos, analisamos tanto qualidade dos resultados quanto tempo de execução, utilizando conjuntos de dados reais e sintéticos. Os operadores propostos alcançaram melhores resultados quanto à qualidade de resultados, e mantiveram os tempos de execução similares. Os operadores que utilizam aproximação aos vizinhos mais próximos produziram resultados de qualidade similar quando comparados aos operadores que utilizando os vizinhos mais próximos, podendo ser executados em menor tempo que estes. / The grouping operator and aggregation functions are the primary tools used to summarize data inside a Relational Database Management Systems (RDBMS). The grouping operator works creating partitions in data using identity comparisons, and allow applying aggregation functions that return a single value that represent the entire group. However, for metric data, grouping by identity is seldom useful. In this case, adopting the concept of the similarity is often a better approach. The literature presents few operators that can group data using similarity. All of them use a distance threshold value to assign the elements in groups. However, these operators do not achieve satisfactory results when the data distribution present a significant variation in the density of objects in different regions of the space. To achieve better results in these situations, we have proposed a novel operator that assign groups using an election involving already assigned groups. We also proposed generalizations to existing and proposed operators to work with an amount of nearest neighbors and approximate neighbors instead of a distance threshold. To support these operators in RDBMS, we propose an extension to Structured Query Language (SQL) and new aggregation functions. Our proposed algorithms can run the proposed and existing operators. We implemented these operators in our framework in C++ using Arboretum library. To evaluate the proposed methods, we assess both results quality and the execution time, using both real and synthetic datasets. The proposed operators achieved better results comparing the quality and maintained similar executing time. The operators that use the approximate nearest neighbors produced similar quality results comparing with the operators that use the exact neighbors and can execute faster than that.

XML-Dokumente haben sich in den letzten Jahren zu einem wichtigen Datenformat für die standardisierte Übertragung von vielfältigen Informationen entwickelt. Dementsprechend gibt es einen großen Bedarf nach Speicherlösungen für XML-Daten. Neben den nativen XML-Datenbanken bieten sich aber zunehmend auch relationale Datenbanksysteme mit unterschiedlichen Ansätzen für die Speicherung der Dokumente an. Die Art der Speicherung ist jedoch nur ein Aspekt der XML-Datenhaltung - die Anwender wollen auch mit ihren gewohnten XML-Schnittstellen auf die Daten zurückgreifen. Das XMLRDB-Projekt bietet dafür ein Nummerierungsschema für XML-Knoten an, welches es erlaubt, aus den in Relationen gespeicherten Daten Strukturinformationen zu gewinnen. In dieser Diplomarbeit werden diese Informationen für eine XPath-Schnittstelle in XMLRDB genutzt, welche dadurch in der Lage ist, XPath-Anfragen nach SQL zu konvertieren und effizient deren Lösungsmenge zu bestimmen. Für diese Schnittstelle werden verschiedene Verfahren für die Umsetzung der XPath-Strukturen vorgestellt. An Hand einer Implementierung wird gezeigt, wie die Fähigkeiten von unterschiedliche Datenbanksystemen gewinnbringend in das Schema integriert werden können. Mittels eines Benchmarks findet schließlich eine Analyse der XPath-Umsetzung hinsichtlich Effizienz und Performanz statt.

In a traditional relational database management system, queries can only be defined over attributes defined in the schema, but are guaranteed to give single, definitive answer structured exactly as specified in the query. In contrast, an information retrieval system allows the user to pose queries without knowledge of a schema, but the result will be a top-k list of possible answers, with no guarantees about the structure or content of the retrieved documents. In this paper, we present DrillBeyond, a novel IR/RDBMS hybrid system, in which the user seamlessly queries a relational database together with a large corpus of tables extracted from a web crawl. The system allows full SQL queries over the relational database, but additionally allows the user to use arbitrary additional attributes in the query that need not to be defined in the schema. The system then processes this semi-specified query by computing a top-k list of possible query evaluations, each based on different candidate web data sources, thus mixing properties of RDBMS and IR systems. We design a novel plan operator that encapsulates a web data retrieval and matching system and allows direct integration of such systems into relational query processing. We then present methods for efficiently processing multiple variants of a query, by producing plans that are optimized for large invariant intermediate results that can be reused between multiple query evaluations. We demonstrate the viability of the operator and our optimization strategies by implementing them in PostgreSQL and evaluating on a standard benchmark by adding arbitrary attributes to its queries.

Data processing for Big Data plays a vital role for decision-makers in organizations and government, enhances the user experience, and provides quality results in prediction analysis. However, many modern data processing solutions make a significant investment in hardware and maintenance costs, such as Hadoop and Spark, often neglecting the well established and widely used relational database management systems (RDBMS's). In this dissertation, we study three fundamental graph problems in RDBMS. The first problem we tackle is computing shortest paths (SP) from a source to a target in large network graphs. We explore SQL based solutions and leverage the intelligent scheduling that a RDBMS performs when executing set-at-a-time expansions of graph vertices, which is in contrast to vertex-at-a-time expansions in classical SP algorithms. Our algorithms perform orders of magnitude faster than baselines and outperform counterparts in native graph databases. Second, we studied the PageRank problem which is vital in Google Search and social network analysis to determine how to sort search results and identify important nodes in a graph. PageRank is an iterative algorithm which imposes challenges when implementing it over large graphs. We study computing PageRank using RDBMS for very large graphs using a consumer-grade machine and compare the results to a dedicated graph database. We show that our RDBMS solution is able to process graphs of more than a billion edges in few minutes, whereas native graph databases fail to handle graphs of much smaller sizes. Last, we present a carefully engineered RDBMS solution to the problem of triangle enumeration for very large graphs. We show that RDBMS's are suitable tools for enumerating billions of triangles in billion-scale networks on a consumer grade machine. Also, we compare our RDBMS solution's performance to a native graph database and show that our RDBMS solution outperforms by orders of magnitude. / Graduate

M. Tech. (Information Technology, Department of Information and Communications Technology, Faculty of Applied an Computer Sciences), Vaal University of Technolog / One of the main research topics in Semantic Web is the semantic extraction of knowledge stored in relational databases through ontologies. This is because ontologies are core components of the Semantic Web. Therefore, several tools, algorithms and frameworks are being developed to enable the automatic conversion of relational databases into ontologies. Ontologies produced with these tools, algorithms and frameworks needs to be valid and competent for them to be useful in Semantic Web applications within the target knowledge domains. However, the main challenges are that many existing automatic ontology construction tools, algorithms, and frameworks fail to address the issue of ontology verification and ontology competency evaluation. This study investigates possible solutions to these challenges. The study began with a literature review in the semantic web field. The review let to the conceptualisation of a framework for semantic knowledge extraction to deal with the abovementioned challenges. The proposed framework had to be evaluated in a real life knowledge domain. Therefore, a knowledge domain was chosen as a case study. The data was collected and the business rules of the domain analysed to develop a relational data model. The data model was further implemented into a test relational database using Oracle RDBMS. Thereafter, Protégé plugins were applied to automatically construct ontologies from the relational database. The resulting ontologies are further validated to match their structures against existing conceptual database-to-ontology mapping principles. The matching results show the performance and accuracy of Protégé plugins in automatically converting relational databases into ontologies. Finally, the study evaluated the resulting ontologies against the requirements of the knowledge domain. The requirements of the domain are modelled with competency questions (CQs) and mapped to the ontology using SPARQL queries design, execution and analysis against users’ views of CQs answers. Experiments show that, although users have different views of the answers to CQs, the execution of the SPARQL translations of CQs against the ontology does produce outputs instances that satisfy users’ expectations. This indicates that Protégé plugins generated ontology from relational database embodies domain and semantic features to be useful in Semantic Web applications.

This work focuses on NoSQL databases, their use for analytical tasks and on comparison of NoSQL databases with relational and OLAP databases. The aim is to analyse the benefits of NoSQL databases and their use for analytical purposes. The first part presents the basic principles of Business Intelligence, Data Warehousing, and Big Data. The second part deals with the key features of relational and NoSQL databases. The last part of the thesis describes the properties of four basic types of NoSQL databases, analyses their advantages, disadvantages and areas of application. The end of this part in-cludes specific examples of the use of NoSQL databases, together with the reasons for the selection of those solutions.